{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "58e60c59",
   "metadata": {},
   "source": [
    "<center><font size=5>姓名：冉成林 学号：20201120466 专业：电子科学与技术</font></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ae2092",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db002c02",
   "metadata": {},
   "source": [
    "<center><font size=6>不动点迭代法求根号二的近似值</font></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e8c75fe",
   "metadata": {},
   "source": [
    "  \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c190e6b",
   "metadata": {},
   "source": [
    "<center><font size=5>Use markdown,matplotlib and numpy in notebook</font></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3fa608b",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d2986c",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab9f44ab",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3520c1d",
   "metadata": {},
   "source": [
    "<div align=center> <img src=\"https://img.wenanjuzi.com/upload/2c4104499831n2525386238m253.jpg\" width=\"800\"></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69ed2603",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02e5b3a0",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8daf0549",
   "metadata": {},
   "source": [
    "<center><font size=4>日期：2022.3.18</font></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07f4b180",
   "metadata": {},
   "source": [
    "## 迭代方程推导:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f3e0199",
   "metadata": {},
   "source": [
    "$x=\\sqrt2$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad85d917",
   "metadata": {},
   "source": [
    "$x^2=2$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "349eee18",
   "metadata": {},
   "source": [
    "$x^2+3x=2+3x$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9948de5b",
   "metadata": {},
   "source": [
    "$x=\\frac {2+3x} {3+x}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f556ae9",
   "metadata": {},
   "source": [
    "$x=3-\\frac{7}{x+3}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0892fe34",
   "metadata": {},
   "source": [
    "$xnew=3-\\frac {7} {xold+3}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b2f94029",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4708d851",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c486b6f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def func(xold):\n",
    "    return 3-7/(xold+3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2ef54022",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最终结果为： 1.4142409269177614 循环次数为： 11\n"
     ]
    }
   ],
   "source": [
    "xold=10\n",
    "xnew=0\n",
    "i=0\n",
    "d=0\n",
    "data=np.zeros((50,4))\n",
    "while True:\n",
    "    xnew=func(xold)\n",
    "    d=abs(xnew-xold)\n",
    "    data[i,:]=[i,xnew,xold,d]\n",
    "    i+=1\n",
    "    xold=xnew\n",
    "    if d<0.0001:\n",
    "        break\n",
    "print(\"最终结果为：\",xnew,\"循环次数为：\",i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a0003149",
   "metadata": {},
   "outputs": [],
   "source": [
    "arrayI=data[:i,0]\n",
    "arrayXnew=data[:i,1]\n",
    "arrayD=data[:i,3]\n",
    "arrayXold=data[:i,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aa58eb3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22faccf8460>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"i\",fontsize=18)\n",
    "plt.ylabel(\"xnew\",fontsize=18)\n",
    "plt.plot(arrayI,arrayXnew)\n",
    "plt.legend([\"i_xnew\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2c913120",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22facd500d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"i\",fontsize=18)\n",
    "plt.ylabel(\"df\",fontsize=18)\n",
    "plt.plot(arrayI,arrayD)\n",
    "plt.legend([\"i_df\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "30e32e6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "arrayX=np.arange(1,100,0.1)\n",
    "Y=func(arrayX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4dfd1f99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x22face0ba60>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"X\",fontsize=18)\n",
    "plt.ylabel(\"Y\",fontsize=18)\n",
    "plt.plot(arrayX,Y)\n",
    "plt.plot(arrayXold,arrayXnew)\n",
    "plt.legend([\"Y=func(X)\",\"Xold_Xnew\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cf64e40e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"X\",fontsize=18)\n",
    "plt.ylabel(\"Y\",fontsize=18)\n",
    "gx=func(arrayXold)\n",
    "plt.plot(arrayXold,arrayXold)\n",
    "plt.plot(arrayXold,gx)\n",
    "i=0\n",
    "while True:\n",
    "    plt.hlines(gx[i], arrayXold[i+1], arrayXold[i])\n",
    "    plt.vlines(arrayXold[i+1],gx[i+1],arrayXold[i+1])\n",
    "    i+=1\n",
    "    if arrayXold[i]-1.414<0.001:\n",
    "        break"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
